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Running
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Zero
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import math
import random
from PIL import Image
import blobfile as bf
from mpi4py import MPI
import numpy as np
from torch.utils.data import DataLoader, Dataset
def load_data(
*,
data_dir,
batch_size,
image_size,
class_cond=False,
deterministic=False,
random_crop=False,
random_flip=True,
):
"""
For a dataset, create a generator over (images, kwargs) pairs.
Each images is an NCHW float tensor, and the kwargs dict contains zero or
more keys, each of which map to a batched Tensor of their own.
The kwargs dict can be used for class labels, in which case the key is "y"
and the values are integer tensors of class labels.
:param data_dir: a dataset directory.
:param batch_size: the batch size of each returned pair.
:param image_size: the size to which images are resized.
:param class_cond: if True, include a "y" key in returned dicts for class
label. If classes are not available and this is true, an
exception will be raised.
:param deterministic: if True, yield results in a deterministic order.
:param random_crop: if True, randomly crop the images for augmentation.
:param random_flip: if True, randomly flip the images for augmentation.
"""
if not data_dir:
raise ValueError("unspecified data directory")
all_files = _list_image_files_recursively(data_dir)
classes = None
if class_cond:
# Assume classes are the first part of the filename,
# before an underscore.
class_names = [bf.basename(path).split("_")[0] for path in all_files]
sorted_classes = {x: i for i, x in enumerate(sorted(set(class_names)))}
classes = [sorted_classes[x] for x in class_names]
dataset = ImageDataset(
image_size,
all_files,
classes=classes,
shard=MPI.COMM_WORLD.Get_rank(),
num_shards=MPI.COMM_WORLD.Get_size(),
random_crop=random_crop,
random_flip=random_flip,
)
if deterministic:
loader = DataLoader(
dataset, batch_size=batch_size, shuffle=False, num_workers=1, drop_last=True
)
else:
loader = DataLoader(
dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True
)
while True:
yield from loader
def _list_image_files_recursively(data_dir):
results = []
for entry in sorted(bf.listdir(data_dir)):
full_path = bf.join(data_dir, entry)
ext = entry.split(".")[-1]
if "." in entry and ext.lower() in ["jpg", "jpeg", "png", "gif"]:
results.append(full_path)
elif bf.isdir(full_path):
results.extend(_list_image_files_recursively(full_path))
return results
class ImageDataset(Dataset):
def __init__(
self,
resolution,
image_paths,
classes=None,
shard=0,
num_shards=1,
random_crop=False,
random_flip=True,
):
super().__init__()
self.resolution = resolution
self.local_images = image_paths[shard:][::num_shards]
self.local_classes = None if classes is None else classes[shard:][::num_shards]
self.random_crop = random_crop
self.random_flip = random_flip
def __len__(self):
return len(self.local_images)
def __getitem__(self, idx):
path = self.local_images[idx]
with bf.BlobFile(path, "rb") as f:
pil_image = Image.open(f)
pil_image.load()
pil_image = pil_image.convert("RGB")
if self.random_crop:
arr = random_crop_arr(pil_image, self.resolution)
else:
arr = center_crop_arr(pil_image, self.resolution)
if self.random_flip and random.random() < 0.5:
arr = arr[:, ::-1]
arr = arr.astype(np.float32) / 127.5 - 1
out_dict = {}
if self.local_classes is not None:
out_dict["y"] = np.array(self.local_classes[idx], dtype=np.int64)
return np.transpose(arr, [2, 0, 1]), out_dict
def center_crop_arr(pil_image, image_size):
# We are not on a new enough PIL to support the `reducing_gap`
# argument, which uses BOX downsampling at powers of two first.
# Thus, we do it by hand to improve downsample quality.
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
arr = np.array(pil_image)
crop_y = (arr.shape[0] - image_size) // 2
crop_x = (arr.shape[1] - image_size) // 2
return arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size]
def random_crop_arr(pil_image, image_size, min_crop_frac=0.8, max_crop_frac=1.0):
min_smaller_dim_size = math.ceil(image_size / max_crop_frac)
max_smaller_dim_size = math.ceil(image_size / min_crop_frac)
smaller_dim_size = random.randrange(min_smaller_dim_size, max_smaller_dim_size + 1)
# We are not on a new enough PIL to support the `reducing_gap`
# argument, which uses BOX downsampling at powers of two first.
# Thus, we do it by hand to improve downsample quality.
while min(*pil_image.size) >= 2 * smaller_dim_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = smaller_dim_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
arr = np.array(pil_image)
crop_y = random.randrange(arr.shape[0] - image_size + 1)
crop_x = random.randrange(arr.shape[1] - image_size + 1)
return arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size]
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